集里怎么说它

• 《Own or Be Owned：不拥有，就被拥有》(01:41起):本集说大多数小企业不盈利不是不够努力，而是『不依靠系统运营』；凭感觉即兴做决定（cowboying）是最大陷阱，生意的每个方面——定价、销售、雇人、推广——都要系统化并每 90 天复盘。

① 提到它的金句

74 条

你必须建立能帮助你可持续地更快移动的系统，有时你必须慢下来才能快。
you got to build the systems that help you move sustainably faster, and sometimes, you have to go slow to go fast.
—— Peter Deng · [39:22]

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当你在评估日益复杂的系统时，从模块化假设出发几乎总是个坏主意。
Almost always a bad idea to start with a modularity assumption when you’re trying to evaluate increasingly complex systems.
—— Ankur Goyal · [51:59]

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我绝对认为 vibe coding 被过度炒作了。我认为人们没有意识到它在长期内会让你的系统变得多么不可维护。
I definitely think that vibe coding is over-hyped. I think people don’t realize how much it’s going to make your systems unmaintainable in the long-term
—— Edwin Chen · [51:56]

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但每次你将决策能力或自主权移交给智能体系统时，你都在某种程度上放弃了一些你的控制权。
But every time you hand over decision-making capabilities or autonomy to agentic systems, you’re kind of relinquishing some amount of control on your end.
—— Kiriti Badam · [10:12]

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我讨厌根本原因这个想法。复杂系统没有单一的根本原因。
I hate the idea of a root cause. Complex systems do not have one root cause.
—— Jason Cohen · [22:17]

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我们对于 L2++ 系统就处于那个时刻，人们愿意为一辆半自动化的车辆花费数千美元。
We’re at that moment for L2++ systems where people are willing to pay thousands of dollars for a semi-automated vehicle.
—— Qasar Younis · [25:35]

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我们确实相信，递归的世界离我们并不遥远——AI 将设计它自己所需的系统，来训练和运行下一代 AI，包括这个。
We do believe that the world of recursion is not that far, where AI will design the systems it needs to train and run the next generation of AI, including this.
—— Sachin Katti · [00:21]

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所以这不是说我们不能对齐这些系统，而是一个巨大的控制内部监控失败，其中沙箱是不够的。
So it’s not that we can’t align these systems, but it is a massive control internal monitoring failure where the sandbox was insufficient.
—— Adam Gleave · [80:47]

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我们不盲目使用 LLM 代码的原因是我们还不信任它,因为它们底层的系统没有足够的刚性。
The reason that we don’t use LLM code blindly is because we don’t trust it yet, because the systems underneath them don’t have enough rigidity.
—— Vaibhav Gupta · [17:17]

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我认为在智能体循环和弄清楚如何开发系统方面仍然存在天文数字的机会,这些系统使你能够在持续反馈循环中花费 1000 倍或 100 万倍的 token 来推动结果。
I think there’s still just astronomical opportunity in agentic looping and figuring out how you develop systems that enable you to spend 1,000x more or 1,000,000x more on tokens to drive an outcome in a continuous feedback loop.
—— Alexandr Wang · [27:11]

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可能有一点是人们还没有完全意识到,拥有基于智能体的系统是多么可能,这些系统不仅可以针对你关心的问题运行一两个小时,而且在某些问题领域,并且在拥有高能力模型作为底层支撑的情况下,你可以让它们运行几天或几周,并完成真正非常复杂的任务。
Probably one thing is people don’t quite realize how possible it is to have agent-based systems that can run not just for an hour or two hours on a problem you care about, but for some problem domains and with highly capable models underlying them, you can get them to run for days or weeks and do really, really complicated tasks.
—— Jeff Dean · [04:55]

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市面上有 ERP 系统，但没有哪家公司爱他们的 ERP 系统。
There are ERP systems out there, but there’s no company that loves their ERP system.
—— Max Hodak · [37:49]

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更重要的不再是你出货多快，而是你决定构建什么、以及怎么构建，才能让你建的系统持续组合在一起、互相协作运作。
How fast you ship, but what you decide to build and how you decide to build it in order to be able to continue composing the systems that you build together and having them interoperate and work together.
—— Ali Haghani · [14:31]

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因为如果你没有好的系统来做增量改进，你就会一直玩打地鼠的游戏。
Because if you don’t have good systems in place to make incremental improvements, you’re constantly playing this game of whack-a-mole.
—— Ali Haghani · [10:36]

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随着时间推移,我预期拥有强大网络安全能力的、被广泛部署的 AI 模型,将带来更安全的系统,而不是更不安全。
Over time, I expect that widely deployed AI models with strong cybersecurity capabilities will lead to systems that are more secure, not less.
—— 嘉宾 · [12:43]

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因为架构这些庞大的系统很有趣，但一个月后它就只是一个提示词了。
Because it’s fun to architect these huge systems, but in a month it’s just a prompt.
—— Wes Bos · [09:52]

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智能体也总会更倾向于 Render 这样的更高层系统来启动一个 web 服务或启动一个工作流，因为试图在 AWS 上做这件事会花更长时间，因为你必须把一堆东西接线到一起，而每个东西都要花一段时间才能起来。
Agents will always prefer higher level systems like Render to spin up a web service or spin up a workflow because trying to do that on AWS will take much longer because you have to wire up a bunch of things together and each thing takes a while to come up.
—— Anurag Gohl · [40:40]

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但更令人兴奋的是，它能让你达到这个目标：随着使用而复利增长的系统。
But more excitingly, it gets you to this goal of systems that compound with use.
—— Arjun Karanam · [02:58]

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我总说的三件事是：我们想构建可重复、可观测、可扩展的系统——事实证明这和 AI 不太合拍，但这些一直是我们追求的原则。
The three things I always say is we wanted to build repeatable, observable, scalable systems, which doesn’t play very well with AI as it turns out, but those were always the tenants that we were going for.
—— Connor Hegey · [05:55]

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如果他们是 AI 原生者或者是系统思考者，并且能够解构是什么让他们的工作运转起来，那么这个人加上经验将胜过那些只有经验但不是 AI 原生者的人。
If they’re AI native or a systems thinker and can deconstruct what makes their job hum, that person plus experience will outcompete someone that just has experience and is not an AI native.
—— Matt Swulinski · [52:41]

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每个人都必须成为系统思考者。
Everybody has to become a systems thinker.
—— 嘉宾 · [09:12]

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这个共同的目标就是构建能消除软件流程中劳苦和繁重工作的系统，让更多的人有能力去构建。
And that shared goal is building systems that remove toil and drudgery from our software process so that more people have the ability to build.
—— Safia Abdalla · [19:21]

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它显然非常擅长解决公理系统，但我认为这并不提供强有力的指示，即这是否在解决市场一直无法解决的问题，因为这些领域周围实际上一直没有市场。
It’s clearly very good at solving axiomatic systems, but I don’t think it provides a strong indication of is this solving things that the market hasn’t been able to solve because there really hasn’t been a market around these.
—— Martin Casado · [05:02]

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智能需要在所有这些事情背后运行,许多不同的系统需要被连接起来,所有那些 token 都要花钱。
Intelligence needs to be run behind all of these things, and many different systems need to be connected, and all those tokens cost money.
—— Cliff Obrecht · [04:13]

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我想要我的 AI，就像我想要我的计算器非常擅长大数乘法一样，我实际上希望我们的 AI 系统比人类更理性、更善于表示和处理概率。
I want my AI, just like I want my calculator to be really good at multiplying large numbers, I would actually like our AI systems to be, you know, more rational, better at representing and manipulating probabilities than humans are.
—— Zubin Gharemani · [19:26]

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另一件事是，我们在某种程度上已经到了使用智能体系统的寒武纪阶段——兴奋、技能以及用 AI 解决一切问题的方法正在爆发式增长。
And the second thing is we’re sort of reached the Cambrian stage of using agentic systems, which means there’s an explosion of excitement and skills and finding out ways to solve anything with AI.
—— Justin Joyce · [18:20]

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我们认为多智能体系统是一个分布式数据问题。
We think of multi-agent systems as a distributed data problem.
—— James · [01:15]

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所以我认为这表明并不是有非常奇特的、完全意想不到的驱动力进入了这些 AI 系统，而是那些与人们试图插入 AI 系统的驱动力相邻的驱动力，可能会被 AI 概括为一种以令人担忧的方式保存它们的价值观和自我保护的方案。
And so I think that the demonstration was less that there were like very bizarre, totally unintended drives making their way into these AI systems and more like with drives that are sort of adjacent to the drives people were trying to insert AI systems, those could get generalized into the AIs pursuing a like you know, a scheme for preserving their values and self-preservation in ways that are that are concerning.
—— Ryan Greenblatt · [29:08]

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我认为我们的担忧是，在默认轨迹上，你可能会在很短的时间内从与人类竞争的 AI 系统直接跨越到狂野的超人类 AI 系统。
Where I think a concern that we have is on the default trajectory, you maybe go straight from AI systems that are competitive with humans to AI systems that are wildly superhuman in a very short period of time.
—— Ryan Greenblatt · [32:33]

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像 OpenAI 和 Anthropic 这样的 AI 公司将继续，但不是在他们能够训练的 AI 系统的能力方面拥有这种强优势，他们将不得不在其他轴线上竞争，比如用户体验、定制化、可能快速整合事物。
AI companies like OpenAI and Anthropic would continue, but rather than having this strong advantage in terms of the capabilities of the AI systems they’re able to train, they would instead have to compete on other axes like user experience, customization, potentially quickly integrating things.
—— Ryan Greenblatt · [43:36]

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可验证性最终是当前 AI 系统成功的唯一最重要属性。
verifiability is ultimately the single most important property of success with current AI systems.
—— Eno Reyes · [10:55]

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我实际上会争辩说，现在的 AI 前沿是能够在没有验证的地方建立验证的 AI 系统，因此它们可以推进到今天人类认为对 AI 来说太难解决的任务中。
I actually would argue the frontier right now of AI is AI systems that can build verification where there is none, and thus they can progress into tasks that today humans consider to be too difficult for AI to resolve.
—— Eno Reyes · [11:46]

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即便是在公司层面，我告诉人们的一件事是，听着，你必须经历转换系统的痛苦。而且它不会那么高效。是的，当然，旧的方式会更快，因为那是肌肉记忆。但如果你进入下一个层级，不仅你会有一个更适合你的系统，而且每个人都会有更多技能。
One of the things, even at a company level, that I tell people is, look, you’ve got to go through the pain of switching your systems. And it’s not going to be as efficient. And yes, of course, the old way will be faster because it’s muscle memory. But if you get to the next level, not only will you have a system that works better for you, but everybody will have more skills.
—— Claire Vo · [24:15]

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大多数 LLM 系统本质上就是一个搜索问题。
Most LLM systems are just a search problem.
—— Jean-Denis Greze · [01:22]

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我认为在未来六个月内，我们会看到一堆这样的系统：公司把一项政策托付给 LLM，让它自动把越来越多的、原本属于私密的信息披露到公开空间里。
And I think in the next six months we’ll have a bunch of systems where companies have trusted an LLM with a policy to automatically surface more and more information that otherwise would have been private into a public space.
—— Jean-Denis Greze · [11:12]

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它之所以奏效，是因为围绕编程的所有基础设施和系统本来就是为智能体而生的。
It only worked because all the infrastructure and systems around coding were literally meant for agents.
—— Karan Vaidya · [01:20]

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而且我几乎认为，对这类系统来说，质量和数量是分不开的，因为上下文就是一切。
And I almost think you can’t separate quality and quantity when it comes to these systems because context is everything.
—— Sharadh Krishnamurthy · [41:38]

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一个是我们会有泛化能力更好的系统，因为每当你增加一个搜索引擎、或向量数据库(它就是一种搜索引擎)、或某种步骤间的路由逻辑，这种人工整理就真的限制了系统做很多不同事情的能力。
One is we will have systems that can generalize better because every time you add a search engine or a vector database, which is a search engine, or some conditional logic to route between the steps, this human curation really limits the ability of that system to do a lot of different things.
—— Alexander Whedon · [30:24]

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这关系到我一直坚持的一个观点,就是我们应该从系统的角度思考,而不是从模型的角度思考。
So, and this relates to a line I’ve had consistently, which is that we should think in terms of systems, not in terms of models.
—— Chris Potts · [45:11]

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这之所以有效，是因为它不是提示词，而是系统。
This works because it’s not prompting, it’s systems.
—— 嘉宾 · [51:57]

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但我认为，通过观察真实系统如何失败——包括我们自己的和全行业的系统——并真正试图提取出「是什么把系统踢进了这种状态」，我们学到的东西多得多。
But I think we’ve learned a huge amount more by looking at how real systems fail, both you know, our own and systems across the industry, and really trying to extract like, what was it that kicked the system into this state?
—— Marc Brooker · [12:30]

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而且实际上，今年我们预计来自 AI 系统的搜索量将超过人类的搜索量。
And actually, this year, we expect the number of searches from AI systems to exceed that of humans.
—— Will Bryk · [00:54]

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如果对人类最优的搜索引擎对 AI 系统同样最优，那就太疯狂了。
And it would be crazy if the same search engine that was optimal for humans was also optimal for these AI systems.
—— Will Bryk · [09:46]

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它对人类来说快得过分了，对吧？但我们服务的不是人类。我们服务的是 AI 系统。
It’s way too fast for humans, right? But we’re not serving humans. We’re serving AI systems.
—— Will Bryk · [11:35]

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对 AI 的投资，90% 失败，没有投资回报。而其中很大一部分原因就是自我参照系统。
Investments in AI, 90% failure, no return on investment. And a lot of that is because of self-referencing systems.
—— Brené Brown · [18:22]

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SEO 就是利用 Google 系统。
SEO is just exploiting Google systems.
—— Liam Dunne · [26:01]

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我怀疑我们还没有发现构建这些系统的最佳方式。肯定存在替代方案，肯定存在构建这些模型的其他方式。
I doubt we’ve discovered the best way of building these systems. There’s got to be alternatives. There’s got to be other ways of building these models.
—— Stefano Ermon · [15:25]

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嗯，我认为我们会失去一些更精细的智力灵活性，但我们会成为更好的系统思考者。
Well, I think that we’re going to lose some finer dexterity of intellectual dexterity, but we’re going to be better systems thinkers.
—— Jensen Huang · [24:25]

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所有的控制系统在拥有足够智能的情况下都是稳健的。
All control systems are robust when you have enough intelligence on them.
—— Eddy Lazzarin · [08:22]

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用大模型来调度一支更小模型的舰队，我认为这是我们相当坚定信奉的范式，它已经被证明是编码系统的运作方式，而且我认为在未来一年它会进入几乎每一个智能体。
Over a fleet of smaller models is I think a paradigm that we believe in quite firmly and it’s already proving out to be how coding systems will work and I think it’ll find its way into almost every agent over the coming year.
—— Hamad Bashir · [24:29]

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所以我们正在重新思考:如何把最初的第一人称操控模式,转换成一种更具战略性的多智能体视图,让你可以带着一个目标指挥整个机队,而不用担心飞行本身。
So we’re kind of rethinking as to what this means in terms of this initial first-person viewing engagement with these systems to how do we convert this to a most strategic multi-agent view that you can kind of command the entire fleet with an objective in mind without having to worry about the flight.
—— Suchet Bargoti · [08:26]

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第一个是,推荐系统的扩展方式和 LLM 一样,而且这个领域在扩展曲线上还处于非常早期的阶段。
The first is that recommendation systems scale just like LLMs do, and that the field is very early in that scaling curve.
—— Devansh Tandon · [00:30]

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所以也许现在操作系统、网络和计算机语言会迎来一次复兴。而且我们应该回到那些旧的研究，我们应该开始重建那些在设计上就是安全的系统。
So maybe now is going to be a renaissance in operating systems and network and computer languages. And we should like go back to the old research and we should kind of start rebuilding systems that are secure by design.
—— Steven Sinofsky · [37:46]

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关键在于，这些都是对智能体协议的小改动，但它们可以对多智能体系统的社会层面行为产生更大的影响。
The key is that these are small changes to the agent protocol, but they can have larger effects on a society-level behavior for multi-agentic systems.
—— Erina Karati · [16:22]

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通过把其他源代码系统和项目跟踪系统中正在发生的活动以 webhook 的形式喂给它，我们开始达到那种自主性水平，我们的工厂在执行产品工程工作，而不仅仅是代码工作。
By feeding our web hooks activities that are happening in other source code systems and project tracking systems, we’re starting to get to that level of autonomy where our factory is performing product engineering work, not just code work.
—— Ryan Cooke · [05:07]

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但正在发生的是，人类的角色并没有消失，它正在向栈的上层移动，可能移动了栈的好几层——从直接检查代码，转到设计和调优那些检查代码的系统，以及设计「好」的定义。
But what’s happening is the human role isn’t disappearing, it is moving up the stack, possibly several levels of the stack, from inspecting the code directly to designing and tuning the systems that inspect the code and designing the definition of good.
—— Laurie Voss · [20:21]

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嘿，在某个外部 AI 来黑我们之前，我们能不能先用你们的产品，确保我们先测试自己的系统？
hey, before some outside AI comes and hacks us, can we use your product and make sure we test our own systems first?
—— Devin Thomas · [19:48]

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还是更自动化的方式，比如用记忆或 prompt 优化之类的工具，这个复利循环非常重要，要构建到你的系统中并由你掌控。
Or more automatically, whether that’s with memory or prompt optimization things, this compounding loop is really important to build into your systems and to own.
—— 嘉宾 · [06:23]

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但在大规模下，当你有数千台服务器时，一年一两次的故障就变成了每周或每月一次的故障，所以你需要有相应的系统来很好地应对这种情况。
But at a large scale, when you have thousands of servers, a once or twice a year failure becomes a weekly or monthly failure, and so you need to have systems in place to deal with this very well.
—— Ben · [05:41]

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所以这是那种——底层现实中是非常复杂的基础设施在扩展你的系统——但只要你为它提供合适的接口，比如简单的配置文件，这些能力对任何想要扩展的公司来说都变得容易接触得多。
So this is one of those things, very complex infrastructure in reality under the hood that is scaling your systems, but you provide the right interfaces for it, like simple configuration files, and those things become much more accessible to any company trying to scale.
—— Ben · [13:55]

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但现实中，在工程工作里，我们 70% 的时间花在另一头，不是生成新软件，而是在生产系统上，基本上就是运行和修复软件。
But in reality, for engineering, 70% of our time is spent on the other side, not generating new software, but rather in production systems, basically running and fixing software.
—— Varun Krovvidi · [02:31]

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我们真诚地相信，你在生产系统上所做的所有这些工作——比如修复问题或者日常运行你的软件——在大部分情况下都应该由智能体来完成。
So we genuinely believe that all of this work that you do on production systems, like fixing issues or running your software on a day-to-day basis, should be done by agents in most part.
—— Varun Krovvidi · [05:57]

指向原始笔记的链接

这些特性中的每一个都证明了，这不仅仅是一个 UI 加一张电子表格，而是一个支撑它的系统问题。
Each one of these features proves that this is not just a UI and a spreadsheet, but rather it is a systems problem that powers it.
—— Hossein · [16:32]

指向原始笔记的链接

如果每个人都用同样的模型，那么伟大的模型为大家设定了下限，但接下来你如何去优化那些模型并构建真正出色的 AI 系统，那才是设定上限的东西。
If everybody is using the same model, like great models sort of set the floor for everybody, but then how you go and optimize those models and build really amazing AI systems, that’s what’s going to set the ceiling.
—— Yash · [20:51]

指向原始笔记的链接

对于这类强化系统和受监管行业来说，这种信任是初创公司很难获得的东西，无论你的技术有多酷。
for these kind of hardened systems and regulated industries is a thing that is just very hard to get as a startup, regardless of how cool your technology is.
—— Woodson Martin · [12:04]

指向原始笔记的链接

这类系统太多了，那种认为我们在某个时点达到静止状态、一切都完美安全的想法，我认为是白日梦。
And there’s so many of these systems, like the idea that at some point we reach a stasis and everything is perfectly secure, I think is a pipe dream.
—— Woodson Martin · [26:26]

指向原始笔记的链接

对业务不利的，是账单惊吓，对我们使用这些系统的方式不利，这些系统现在是任务关键型的，而这些公司没有一家建立了像银行那样的金融基础设施，能够在提款之前、在消费之前先做检查，对吧？
Bad for business, it’s sticker shock, it’s bad for practice of how we use the systems, these systems are now mission critical and we’re basically, none of these companies had in place the financial infrastructure that allows exactly like banks guys to check before we draw down, before we consume, right?
—— Dor Sasson · [05:33]

指向原始笔记的链接

我还没见过任何基准测试表明图数据库——原生图数据库——能在那些关系型数据库系统拥有正确的 SQL 或 API 构造、从而让你完全在服务器端做图遍历、不用来回往返的情况下，胜过关系型数据库系统。
I have not seen any benchmarks that show graph databases, the native graph databases, outperform the relational databases systems when those related systems have the right constructs in SQL or the API so that you can do graph traversal all on the server side and not go back and forth.
—— Andy Pavlo · [22:23]

指向原始笔记的链接

是的，数据库系统领域有句老话：需要十年，但它是一个系统。你可以在三年里构建出前 90%，然后剩下的 10% 要花接下来七年。
Yeah, the old adage from database systems is that it takes 10 years, but it’s a system. You can build the first 90% in three years and then the remaining 10% takes the next seven years.
—— Andy Pavlo · [29:34]

指向原始笔记的链接

超过 60% 的开源数据库系统的提交来自智能体。
Over 60% of the open source database systems have commits coming from agents.
—— Andy Pavlo · [30:37]

指向原始笔记的链接

所以我觉得要学会建立正确的系统、正确的习惯，在追求这个使命的过程中搞垮身体是没有任何荣耀可言的。
And so I think learning to build the right systems, the right habits, there’s no glory in becoming unhealthy in pursuit of this mission.
—— Alyssa Ravasio · [22:33]

指向原始笔记的链接

这不仅能让单个智能体下次做完全相同的事情时变得更好，更要想想那些拥有成百上千个智能体、它们可以访问某种共享工具组的系统。
This is important not just so that a single agent can get better the next time you do it to do the exact same thing, but rather think about systems where you have hundreds or thousands of agents that have some shared grouping of tools that they have access to.
—— Will Lyon · [05:16]

指向原始笔记的链接

当我们审视这个问题时，我们意识到，在分布式系统和微服务架构中，API 就是你的上下文层。
And when we looked at this, what we realized is in a distributed systems and microservices architecture, APIs are the context layer for you.
—— Kamalakannan Nandagopal · [04:01]

指向原始笔记的链接

所以我们正在下一个赌注，我们下的赌注是，通过放弃如今每个人都依赖的先验，比如人类数据，比如其他所有人构建其系统所依托的整个互联网知识，我们可以走得更远。
So we are making a bet, and the bet we’re making is that we can go further by giving up on the priors that everyone’s relying on today, like the human data, like the whole internet of knowledge that everyone else is building their systems upon.
—— David Silver · [28:36]

指向原始笔记的链接

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